A three-stage low-carbon regulation optimization method and system for distribution network based on carbon flow distribution

Abstract

The present invention discloses a three-stage low-carbon control optimization method and system for a distribution network based on carbon flow distribution, and relates to the technical field of low-carbon transformation of power systems. It includes: establishing a low-carbon economic optimal power flow model in the first stage, obtaining the distribution network benchmark power flow result, and passing it to the second stage to perform carbon profiling of the distribution network; in the second stage, by introducing the key matrix vectors of the node active flux matrix, the branch power flow distribution matrix, and the unit injection distribution matrix, the node carbon potential is obtained to obtain the real-time carbon emissions of each branch, node, and main network of the distribution network, and to perform carbon profiling for the distribution network; in the second stage, a carbon cost model is established based on the carbon flow distribution of the distribution network, and finally the carbon emission cost obtained in the second stage is transmitted to the corresponding first stage and third stage; a third stage flexibility resource control model is established to optimize the configuration of flexibility resources. The present invention can ensure that the distribution network reduces the operating costs and carbon emissions of the distribution network on the basis of clarifying the carbon flow distribution.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon transformation of power systems, and more specifically to a three-stage low-carbon regulation and optimization method and system for a distribution network based on carbon flow distribution. Background Art

[0002] The access of massive distributed power sources and emerging loads has led to complex operation of the distribution network, and traditional planned manual control methods are difficult to meet economic, low-carbon and other operational requirements.

[0003] First, the distribution network lacks sophisticated carbon flow distribution calculations, making it difficult to accurately track and quantify carbon emissions at each link in the system, which makes it difficult to reasonably allocate carbon emission responsibilities within the distribution network and identify high-carbon emission sources.

[0004] Secondly, the existing distribution network dispatching technology is usually dominated by economic efficiency, ignoring the impact of the regulation method on the system's carbon emissions. The above problems make it difficult for the distribution network to achieve low-carbon economic development.

[0005] Therefore, it is an urgent problem for technical personnel in this field to propose a three-stage low-carbon regulation optimization method and system for distribution network based on carbon flow distribution to solve the difficulties existing in the prior art. Summary of the invention

[0006] In view of this, the present invention provides a three-stage low-carbon control optimization method and system for distribution network based on carbon flow distribution. On the basis of clarifying the carbon flow distribution of distribution network, the present invention considers the fair distribution of carbon emission responsibilities between the supply and demand sides of distribution network, introduces the costs of both supply and demand sides as guiding signals, and establishes a low-carbon economic optimal power flow model of distribution network focusing on minimizing supply-side costs in the first stage; establishes a carbon flow distribution calculation and carbon cost model for distribution network in the second stage; and establishes a flexible resource control model focusing on minimizing demand-side costs in the third stage. The method of the present invention can ensure that the distribution network can better reduce the operating costs and carbon emissions of the distribution network on the basis of clarifying the carbon flow distribution, while taking into account the economy and low carbon nature of the distribution network operation.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A three-stage low-carbon regulation optimization method for a distribution network based on carbon flow distribution includes the following steps:

[0009] Establish the first-stage low-carbon economic optimal power flow model with supply-side cost minimization as the objective function and under the power system constraints, obtain the distribution network benchmark power flow results, and pass them to the second stage for distribution network carbon profiling;

[0010] In the second stage, the key matrix vectors of node active flux matrix, branch power flow distribution matrix and unit injection distribution matrix are introduced to obtain the node carbon potential, and the real-time carbon emissions of each branch, node and main grid of the distribution network are obtained to make a carbon portrait for the distribution network.

[0011] In the second stage, a carbon cost model is established based on the carbon flow distribution in the distribution network, and finally the carbon emission cost obtained in the second stage is transmitted to the corresponding first and third stages;

[0012] Establish a third-stage flexibility resource control model to optimize the allocation of flexibility resources.

[0013] The above method may optionally further include: if demand response is performed, the load power is redistributed, and the adjusted power is brought into the optimal power flow model of the first stage and the carbon cost model of the second stage for re-solving until the load power is no longer adjusted and optimized, and the optimal regulation result at this time is output to obtain the total cost of the distribution network and the total carbon emissions.

[0014] The above method optionally establishes the specific content of the first-stage low-carbon economic optimal power flow model with supply-side cost minimization as the objective function and power system constraints: establishing a first-stage objective function, establishing distributed gas unit output constraints, establishing main network power purchase constraints, establishing node voltage and line current constraints, establishing line power flow constraints, establishing node power balance constraints, establishing Ohm's law constraints, and establishing second-order cone relaxation constraints.

[0015] The above method is optional. In the second stage, the key matrix vectors of the node active flux matrix, the branch power flow distribution matrix, and the unit injection distribution matrix are introduced to obtain the node carbon potential, and the real-time carbon emissions of each branch, node and main network purchased electricity of the distribution network are obtained. The specific contents of the carbon portrait for the distribution network include:

[0016] Nodal carbon potential calculation:

[0017]

[0018] Among them, P N is the node active flux matrix; P B is the branch power flow distribution matrix; P G Inject the distribution matrix into the generator set; E G is the unit carbon emission intensity matrix; T is the transposition;

[0019] Carbon flow rate calculation:

[0020] R L =P L ×E N

[0021] R B =PB ×diag(E N )

[0022] R Loss =P Loss ×E′ N

[0023] Among them, P L is the load distribution matrix; P B is the branch power flow matrix; P Loss is the network loss distribution matrix; R L is the carbon flow rate at the load node; R B is the carbon flow rate of the branch tidal current; R Loss is the carbon flow rate of branch network loss; E' N is the carbon potential of the branch head node.

[0024] In the above method, optionally, the carbon cost model includes a load carbon emission responsibility allocation model and a network loss carbon emission responsibility allocation model.

[0025] The above method is optional. The load carbon emission responsibility allocation model assumes carbon emission responsibility based on the tiered carbon price. The network loss carbon emission responsibility allocation model includes the initial carbon emission value when the distributed power source is not connected to the grid. The incremental value of carbon emissions after the distributed generation is connected to the grid, that is, the change in carbon emissions from network losses after the distributed generation is connected

[0026] The above method can optionally establish a node load carbon cost model based on a stepped carbon price, namely:

[0027]

[0028] in, is the carbon emission cost of load node i at time t; is the carbon emission of load node i at time t; δ is the interval length of the step carbon price; γ is the growth rate of the carbon trading price in each interval; The cost of carbon emissions.

[0029] The above method optionally uses the initial carbon emission value of the distributed power source before it is connected to the grid based on carbon flow tracking. The supply and demand sides will share the cost equally, including:

[0030] Establish a carbon emissions downstream tracking model:

[0031]

[0032] in, is the carbon emission apportionment of the initial network loss of the distribution network by load i; M is the number of nodes; A s is the downstream carbon flow tracking matrix, is the carbon emission of load i, R f is the carbon emission of node f, R fn is the carbon emission of branch fn, R n is the carbon emission of node n;

[0033] Establish a two-way allocation model:

[0034]

[0035] in, The initial network loss carbon emission responsibility borne by the supply side, The initial network loss carbon emission responsibility borne by the demand side;

[0036] The incremental value of carbon emission of distributed power network loss in distribution network is established based on the network loss carbon emission contribution rate determined by the combined optimization weight. Assuming that n distributed power sources are connected to the distribution network, the number of indicators is m, and x ij is the jth index data of the i-th distributed power source, i=1,2,…,n; j=1,2,…,m;

[0037] Obtain the objective weight w of the entropy weight method j :

[0038]

[0039] g j =1-e j

[0040]

[0041] in, is the normalized index data; n is the number of distributed power sources; p ij for Specific gravity; g j is the entropy redundancy value of the j-th indicator; e j is the entropy value of the jth indicator;

[0042] Obtaining the subjective weight h of the order relationship method j :

[0043]

[0044] h j-1 =r j ×h j , j=m,m-1,…,3,2

[0045] Among them, m is the number of indicators; r k is the relative influence value between two adjacent indicators; r jis the influence value between the jth and j-1th indicators; h m is the subjective weight of the mth indicator; h j-1 is the subjective weight of the j-1th indicator;

[0046] Obtain the combination optimization weight z j :

[0047] Integrate subjective weights and objective weights to build an optimization model to achieve the minimum deviation between the combined weights and the weights calculated by different weighting methods, that is:

[0048]

[0049] z j ≥0

[0050]

[0051] The method of constructing Lagrangian function is used to solve the extreme value problem of given conditions, namely:

[0052]

[0053] Then we can obtain:

[0054]

[0055] Establish a distribution model for the incremental carbon emission value of distributed power generation network losses:

[0056]

[0057] Among them, α i is the contribution rate of carbon emission of network loss; Ω1 is the distributed power generation group on the supply side; Ω2 is the distributed power generation group on the demand side; v i Contribution value to network loss carbon emissions.

[0058] The above method optionally includes establishing the specific contents of the third-stage flexibility resource control model, including: establishing a three-stage objective function, node load change constraints, load response balance constraints, new energy unit output constraints, and energy storage device constraints.

[0059] A three-stage low-carbon regulation and optimization system for a distribution network based on carbon flow distribution, which implements any of the above three-stage low-carbon regulation and optimization methods for a distribution network based on carbon flow distribution, including a module for establishing a low-carbon economic optimal power flow model of a distribution network, a carbon flow distribution module, a module for establishing a carbon cost model, and a module for establishing a flexibility resource regulation model, which are connected in sequence; wherein,

[0060] Establish a low-carbon economic optimal power flow model module for the distribution network: establish a first-stage low-carbon economic optimal power flow model with supply-side cost minimization as the objective function and under the constraints of the power system, obtain the distribution network benchmark power flow results, and pass them to the second stage for distribution network carbon profiling;

[0061] Carbon flow distribution module: In the second stage, the node carbon potential is obtained by introducing the key matrix vectors of the node active flux matrix, branch power flow distribution matrix, and unit injection distribution matrix, and the real-time carbon emissions of each branch, node, and main grid of the distribution network are obtained to make a carbon portrait for the distribution network;

[0062] Establishing a carbon cost model module: In the second stage, a carbon cost model is established based on the carbon flow distribution of the distribution network, and finally the carbon emission cost obtained in the second stage is transmitted to the corresponding first and third stages;

[0063] Establish a flexibility resource control model module: Establish the third-stage flexibility resource control model to optimize the allocation of flexibility resources.

[0064] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a three-stage low-carbon control optimization method and system for a distribution network based on carbon flow distribution, which has the following beneficial effects:

[0065] 1) Introduce carbon emission and cost guidance signals to promote coordination between the supply and demand sides, ensure the low-carbon development of the distribution system while ensuring safety and economy, clarify the carbon flow distribution of the distribution network, minimize the output of carbon-intensive resources, reduce the carbon emissions of the system, thereby reducing the total cost of both supply and demand sides and achieving the economic optimal goal;

[0066] 2) Conduct flexible resource regulation on the demand side, consider load shifting in multiple time periods, and the coordination between energy storage devices and new energy units, encourage users to increase electricity consumption when low-carbon energy supply is sufficient, store energy when low-carbon energy is in excess, and release the stored energy when necessary, thereby reducing dependence on carbon-intensive energy and reducing the overall carbon potential and carbon emissions of the system;

[0067] 3) The demand side assumes the responsibility for carbon emissions from node loads. Users can pay more attention to their electricity usage behavior by understanding their own "carbon footprint", and their awareness of low-carbon emission reduction will be improved. Distributed power sources assume the responsibility for network loss carbon emissions according to their carbon emission contribution rate. This can identify and optimize power source configurations that lead to high network loss carbon emissions, measure the benefits of distributed power source grid connection, and at the same time encourage distributed power sources to develop in the direction of loss-reducing and low-carbon grid connection. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0069] Figure 1 A flow chart of a three-stage low-carbon regulation and optimization method for a distribution network based on carbon flow distribution provided by the present invention;

[0070] Figure 2 A structural diagram of a three-stage low-carbon control optimization system for a distribution network based on carbon flow distribution provided by the present invention;

[0071] Figure 3 A three-stage low-carbon regulation optimization strategy diagram based on carbon flow distribution provided by the present invention;

[0072] Figure 4 A flow chart for solving the three-stage low-carbon regulation optimization model provided by the present invention;

[0073] Figure 5 A flow chart for calculating carbon flow distribution in a power system provided by the present invention;

[0074] Figure 6 A flow chart of the distribution network loss carbon emission responsibility allocation provided by the present invention;

[0075] Figure 7 A system topology diagram provided for an embodiment of the present invention;

[0076] Figure 8 A curve chart of load and new energy power generation prediction values ​​provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] See also Figure 1 As shown, the present invention discloses a three-stage low-carbon regulation optimization method for a distribution network based on carbon flow distribution, comprising the following steps:

[0079] Establish the first-stage low-carbon economic optimal power flow model with supply-side cost minimization as the objective function and under the power system constraints, obtain the distribution network benchmark power flow results, and pass them to the second stage for distribution network carbon profiling;

[0080] In the second stage, the key matrix vectors of node active flux matrix, branch power flow distribution matrix and unit injection distribution matrix are introduced to obtain the node carbon potential, and the real-time carbon emissions of each branch, node and main grid of the distribution network are obtained to make a carbon portrait for the distribution network.

[0081] In the second stage, a carbon cost model is established based on the carbon flow distribution in the distribution network, and finally the carbon emission cost obtained in the second stage is transmitted to the corresponding first and third stages;

[0082] Establish a third-stage flexibility resource control model to optimize the allocation of flexibility resources.

[0083] Specifically, flexibility resources include: demand response and distributed energy storage, wind power, and photovoltaic resources.

[0084] See also Figure 3 The three-stage low-carbon regulation optimization strategy diagram based on carbon flow distribution of the present invention is shown in FIG. Figure 4 Shown is the flow chart for solving the three-stage low-carbon regulation optimization model.

[0085] Furthermore, it also includes: if demand response is performed, the load power will be redistributed, and the adjusted power will be brought into the optimal power flow model of the first stage and the carbon cost model of the second stage to be re-solved until the load power is no longer adjusted and optimized, and the optimal regulation result at this time is output to obtain the total cost of the distribution network and the total carbon emissions.

[0086] Furthermore, the specific contents of establishing the first-stage low-carbon economic optimal power flow model with supply-side cost minimization as the objective function and power system constraints are as follows: establishing a first-stage objective function, establishing output constraints for distributed gas-fired units, establishing main grid power purchase constraints, establishing node voltage and line current constraints, establishing line power flow constraints, establishing node power balance constraints, establishing Ohm's law constraints, and establishing second-order cone relaxation constraints.

[0087] Specifically, establish a first-stage objective function:

[0088]

[0089] Among them, C DSO is the total cost on the supply side; C f Cost of electricity generation for gas-fired units; Cost of purchasing electricity for the main grid; The carbon emission costs borne by the supply side;

[0090] Establish output constraints for distributed gas units:

[0091]

[0092] in, is the lower limit of the active output of the gas generator connected to node i at time t, is the upper limit of the active output of the gas generator connected to node i at time t; is the lower limit of reactive power output of the gas generator connected to node i at time t, is the upper limit of reactive power output of the gas generator connected to node i at time t;

[0093] Establish the main grid power purchase power constraint:

[0094]

[0095] in, They are the lower limit of the active power purchased by the distribution network from the main grid at time t and the upper limit of the active power purchased by the distribution network from the main grid at time t respectively; They are the lower limit of reactive power purchased by the distribution network from the main grid at time t and the upper limit of reactive power purchased by the distribution network from the main grid at time t respectively;

[0096] Establish node voltage and line current constraints:

[0097]

[0098] Among them, min ,υ max are the lower limit and upper limit of the square of the node voltage value respectively; τ max is the upper limit of the square of the line current value;

[0099] To create line flow constraints:

[0100]

[0101] in, They are the lower limit and the upper limit of the line active power respectively; They are the lower limit of line reactive power and the upper limit of line reactive power respectively;

[0102] Establish node power balance constraints:

[0103]

[0104] Where Ω is the set of all lines in the distribution network; P t,i , Q t,i are the net active power injected by node i at time t and the net reactive power injected by node i at time t respectively; P t,in , Q t,in are the active power flowing through the downstream branch in of node i at time t and the reactive power flowing through the downstream branch in of node i at time t respectively; P t,mi , Qt,mi are the active power flowing through the upstream branch mi of node i at time t and the reactive power flowing through the upstream branch mi of node i at time t respectively; r mi 、x mi are the upstream branch resistance and upstream branch reactance respectively; They are the active power injected by the gas engine at node i at time t, the active power injected by the wind turbine, the active power injected by the photovoltaic machine, and the charging and discharging power of the energy storage device; is the active power of node load i at time t and the reactive power of node load i at time t;

[0105] Create an Ohm's law constraint:

[0106]

[0107] Among them, t,i is the square of the voltage; τ t,mi is the square of the current value;

[0108] Create a second-order cone relaxation constraint:

[0109] because The existence of makes the model non-convex, so it is subjected to second-order cone relaxation to obtain:

[0110]

[0111] Among them, ||2 is the two-norm.

[0112] For further information, see Figure 5 As shown in the figure, in the second stage, the key matrix vectors of the node active flux matrix, branch power flow distribution matrix, and unit injection distribution matrix are introduced to obtain the node carbon potential, and the real-time carbon emissions of each branch, node, and main grid purchased electricity of the distribution network are obtained. The specific contents of the carbon portrait for the distribution network include:

[0113] Node carbon potential calculation:

[0114]

[0115] Among them, P N is the node active flux matrix; P B is the branch power flow distribution matrix; P G Inject the distribution matrix into the generator set; E G is the unit carbon emission intensity matrix; T is the transposition;

[0116] Carbon flow rate calculation:

[0117] R L =P L ×E N

[0118] R B =P B ×diag(E N )

[0119] R Loss =P Loss ×E′ N

[0120] Among them, P L is the load distribution matrix; P B is the branch power flow matrix; P Loss is the network loss distribution matrix; R L is the carbon flow rate at the load node; R B is the carbon flow rate of the branch tidal current; R Loss is the carbon flow rate of branch network loss; E' N is the carbon potential of the branch head node.

[0121] Furthermore, the carbon cost model includes a load carbon emission responsibility allocation model and a network loss carbon emission responsibility allocation model.

[0122] Furthermore, the load carbon emission responsibility allocation model assumes carbon emission responsibility based on the tiered carbon price, and the network loss carbon emission responsibility allocation model includes the initial carbon emission value when the distributed power source is not connected to the grid. The incremental value of carbon emissions after the distributed generation is connected to the grid, that is, the change in carbon emissions from network losses after the distributed generation is connected

[0123] Furthermore, a node load carbon cost model is established based on the stepped carbon price, namely:

[0124]

[0125] in, is the carbon emission cost of load node i at time t; is the carbon emission of load node i at time t; δ is the interval length of the step carbon price; γ is the growth rate of the carbon trading price in each interval; The cost of carbon emissions.

[0126] For further information, see Figure 6 As shown in the figure, based on carbon flow tracking, the initial carbon emission value of distributed power generation before grid connection is calculated. The supply and demand sides will share the cost equally, including:

[0127] Establish a carbon emissions downstream tracking model:

[0128]

[0129] in, is the carbon emission apportionment of the initial network loss of the distribution network by load i; M is the number of nodes; As is the downstream carbon flow tracking matrix, is the carbon emission of load i, R f is the carbon emission of node f, R fn is the carbon emission of branch fn, R n is the carbon emission of node n;

[0130] Establish a two-way allocation model:

[0131]

[0132] in, The initial network loss carbon emission responsibility borne by the supply side, The initial network loss carbon emission responsibility borne by the demand side;

[0133] The incremental value of carbon emission of distributed power network loss in distribution network is established based on the network loss carbon emission contribution rate determined by the combined optimization weight. Assuming that n distributed power sources are connected to the distribution network, the number of indicators is m, and x ij is the jth index data of the i-th distributed power source, i=1,2,…,n; j=1,2,…,m;

[0134] Obtain the objective weight w of the entropy weight method j :

[0135]

[0136] g j =1-e j

[0137]

[0138] in, is the normalized index data; n is the number of distributed power sources; p ij for Specific gravity; g j is the entropy redundancy value of the j-th indicator; e j is the entropy value of the jth indicator;

[0139] Obtaining the subjective weight h of the order relationship method j :

[0140]

[0141] h j-1 =r j ×h j , j=m,m-1,…,3,2

[0142] Among them, m is the number of indicators; r kis the relative influence value between two adjacent indicators; r j is the influence value between the jth and j-1th indicators; h m is the subjective weight of the mth indicator; h j-1 is the subjective weight of the j-1th indicator;

[0143] Specifically, the impact value depends on expert advice and practical experience in grid operation.

[0144] Obtain the combination optimization weight z j :

[0145] Integrate subjective weights and objective weights to build an optimization model to achieve the minimum deviation between the combined weights and the weights calculated by different weighting methods, that is:

[0146]

[0147] z j ≥0

[0148]

[0149] The method of constructing Lagrangian function is used to solve the extreme value problem of given conditions, namely:

[0150]

[0151] Then we can obtain:

[0152]

[0153] Establish a distribution model for the incremental carbon emission value of distributed power generation network losses:

[0154]

[0155] Among them, α i is the contribution rate of carbon emission of network loss; Ω1 is the distributed power generation group on the supply side; Ω2 is the distributed power generation group on the demand side; v i Contribution value to network loss carbon emissions.

[0156] Specifically, carbon emission costs include the main grid electricity purchase emission costs, gas unit output emission costs, node load emission costs and network loss carbon emission costs.

[0157] Furthermore, the specific contents of establishing the third-stage flexibility resource control model include: establishing a three-stage objective function, node load change constraints, load response balance constraints, new energy unit output constraints, and energy storage device constraints.

[0158] Specifically, a three-stage objective function is established:

[0159]

[0160] Among them, C Load is the total cost on the demand side; C pe The cost of electricity purchased by users; C w is the power generation cost of wind turbine; C v is the power generation cost of the photovoltaic unit; C e is the operating cost of the energy storage device; C curtail Cost of curtailing wind and solar power; is the carbon emission cost on the demand side; C dem Incentive costs for load demand response;

[0161] Constraints on node load variation:

[0162]

[0163] Among them, D t,i is the load response at node i at time t, which takes a positive value when the load is increased and a negative value when the load is decreased; is the active power of the load at node i at time t;

[0164] Load response balance constraints:

[0165]

[0166] Output constraints of new energy units:

[0167]

[0168] in, are the lower limit of active output of the wind turbine connected to node i at time t and the upper limit of active output of the wind turbine respectively; are the lower limit of active output of the photovoltaic generator connected to node i at time t and the upper limit of active output of the photovoltaic generator respectively; is the active output of the wind turbine connected to node i at time t; is the active power output of the photovoltaic generator connected to node i at time t;

[0169] Energy storage device constraints:

[0170]

[0171] in, are the maximum charging power of the energy storage device and the maximum discharging power of the energy storage device respectively; are respectively the energy conversion efficiency of the energy storage device charging and the energy conversion efficiency of the energy storage device discharging; is the capacity of the energy storage device; They are respectively the lower limit of the state of charge of the energy storage device and the upper limit of the state of charge of the energy storage device.

[0172] and Figure 1 Corresponding to the method described above, the present invention also provides a three-stage low-carbon control optimization system for distribution network based on carbon flow distribution, which is used to Figure 1 The specific implementation of the method in the above example is shown in the schematic diagram of its structure. Figure 2 As shown, it includes a module for establishing a low-carbon economic optimal power flow model of a distribution network, a carbon flow distribution module, a module for establishing a carbon cost model, and a module for establishing a flexible resource regulation model, which are connected in sequence; wherein,

[0173] Establish a low-carbon economic optimal power flow model module for the distribution network: establish a first-stage low-carbon economic optimal power flow model with supply-side cost minimization as the objective function and under the constraints of the power system, obtain the distribution network benchmark power flow results, and pass them to the second stage for distribution network carbon profiling;

[0174] Carbon flow distribution module: In the second stage, the node carbon potential is obtained by introducing the key matrix vectors of the node active flux matrix, branch power flow distribution matrix, and unit injection distribution matrix, and the real-time carbon emissions of each branch, node, and main grid of the distribution network are obtained to make a carbon portrait for the distribution network;

[0175] Establishing a carbon cost model module: In the second stage, a carbon cost model is established based on the carbon flow distribution of the distribution network, and finally the carbon emission cost obtained in the second stage is transmitted to the corresponding first and third stages;

[0176] Establish a flexibility resource control model module: Establish the third-stage flexibility resource control model to optimize the allocation of flexibility resources.

[0177] In a specific embodiment, the distribution network adopts an improved IEEE 33 node and includes 7 distributed power sources. The system topology is shown in Figure 7 shown.

[0178] The capacities of distributed gas-fired units are 2.5MW, 1MW, and 0.5MW respectively; the capacities of wind turbines are 1.2MW and 1MW; the capacities of photovoltaic units are 0.5MW and 0.4MW; and the energy storage equipment is 2MW.

[0179] For specific load and new energy power generation forecast value curves, see Figure 8 For cost comparison, see Table 1; for carbon emission comparison, see Table 2.

[0180] Table 1: Cost comparison table

[0181]

[0182] Table 2: Carbon emissions comparison table

[0183] Scheduling results Economic Dispatch Low Carbon Dispatch Savings <![CDATA[Carbon emissions of gas turbine unit / kgCO2]]> 8032.72 6786.57 15.51％ <![CDATA[Carbon emissions of main online electricity purchases / kgCO2]]> 0.00 253.90 <![CDATA[Carbon emissions of node load / kgCO2]]> 7949.70 6744.66 15.16％ Network loss carbon emissions / kg 112.15 85.81 23.49％ <![CDATA[Total carbon emissions on the supply side / kgCO2]]> 8032.72 7040.47 12.35％

[0184] Based on the above content, it can be seen that the distribution network operation costs and carbon emissions under the three-stage low-carbon regulation optimization method are significantly lower than those of the traditional economic regulation method. In other words, the low-carbon regulation optimization method takes into account both low-carbon and economic performance.

[0185] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0186] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A three-stage low-carbon regulation optimization method for distribution network based on carbon flow distribution, characterized in that: The following steps are involved: Establish the first-stage low-carbon economic optimal power flow model with supply-side cost minimization as the objective function and under the power system constraints, obtain the distribution network benchmark power flow results, and pass them to the second stage for distribution network carbon profiling; In the second stage, the key matrix vectors of node active flux matrix, branch power flow distribution matrix and unit injection distribution matrix are introduced to obtain the node carbon potential, and the real-time carbon emissions of each branch, node and main grid of the distribution network are obtained to make a carbon portrait for the distribution network. In the second stage, a carbon cost model is established based on the carbon flow distribution in the distribution network, and finally the carbon emission cost obtained in the second stage is transmitted to the corresponding first and third stages; Establish the third-stage flexibility resource control model to optimize the allocation of flexibility resources; In the second stage, the key matrix vectors of node active flux matrix, branch power flow distribution matrix and unit injection distribution matrix are introduced to obtain the node carbon potential, and the real-time carbon emissions of each branch, node and main grid of the distribution network are obtained. The specific contents of carbon profiling for the distribution network include: Node carbon potential calculation: Among them, P N is the node active flux matrix; P B is the branch power flow distribution matrix; P G Inject the distribution matrix into the generator set; E G is the unit carbon emission intensity matrix; T is the transposition; Carbon flow rate calculation: R L =P L ×E N R B =P B ×diag(E N ) R Loss =P Loss ×E′ N Among them, P L is the load distribution matrix; P B is the branch power flow matrix; P Loss is the network loss distribution matrix; R L is the carbon flow rate at the load node; R B is the carbon flow rate of the branch tidal current; R Loss is the carbon flow rate of branch network loss; E' N is the carbon potential of the branch head node; The carbon cost model includes the load carbon emission responsibility allocation model and the network loss carbon emission responsibility allocation model; The load carbon emission responsibility allocation model assumes carbon emission responsibility based on the tiered carbon price, and the network loss carbon emission responsibility allocation model includes the initial carbon emission value when the distributed power source is not connected to the grid. The incremental value of carbon emissions after the distributed generation is connected to the grid, that is, the change in carbon emissions from network losses after the distributed generation is connected The node load carbon cost model is established based on the stepped carbon price, namely: in, is the carbon emission cost of load node i at time t; is the carbon emission of load node i at time t; δ is the interval length of the step carbon price; γ is the growth rate of the carbon trading price in each interval; for the cost of carbon emissions; Based on carbon flow tracking, the initial carbon emission value of distributed power generation before grid connection The supply and demand sides will share the cost equally, including: Establish a carbon emissions downstream tracking model: in, is the carbon emission apportionment of the initial network loss of the distribution network by load i; M is the number of nodes; A s is the downstream carbon flow tracking matrix, is the carbon emission of load i, R f is the carbon emission of node f, R fn is the carbon emission of branch fn, R n is the carbon emission of node n; Establish a two-way allocation model: in, The initial network loss carbon emission responsibility borne by the supply side, The initial network loss carbon emission responsibility borne by the demand side; The incremental value of carbon emission of distributed power network loss in distribution network is established based on the network loss carbon emission contribution rate determined by the combined optimization weight. Assuming that n distributed power sources are connected to the distribution network, the number of indicators is m, and x ij is the jth index data of the i-th distributed power source, i=1,2,…,n; j=1,2,…,m; Obtain the objective weight w of the entropy weight method j : in, is the normalized index data; n is the number of distributed power sources; p ij for Specific gravity; g j is the entropy redundancy value of the j-th indicator; e j is the entropy value of the jth indicator; Obtaining the subjective weight h of the order relationship method j : Among them, m is the number of indicators; r k is the relative influence value between two adjacent indicators; r j is the influence value between the jth and j-1th indicators; h m is the subjective weight of the mth indicator; h j-1 is the subjective weight of the j-1th indicator; Obtain the combination optimization weight z j : Integrate subjective weights and objective weights to build an optimization model to achieve the minimum deviation between the combined weights and the weights calculated by different weighting methods, that is: z j ≥0 The method of constructing Lagrangian function is used to solve the extreme value problem of given conditions, namely: Then we can obtain: Establish a distribution model for the incremental carbon emission value of distributed power generation network losses: Among them, α i is the contribution rate of carbon emission of network loss; Ω1 is the distributed power generation group on the supply side; Ω2 is the distributed power generation group on the demand side; v i Contribution value to network loss carbon emissions.

2. According to claim 1, a three-stage low-carbon control optimization method for distribution network based on carbon flow distribution is characterized in that: Also includes: If demand response is performed, the load power will be redistributed, and the adjusted power will be brought into the optimal power flow model of the first stage and the carbon cost model of the second stage to be re-solved until the load power is no longer adjusted and optimized. The optimal regulation result at this time is output to obtain the total cost of the distribution network and the total carbon emissions.

3. The three-stage low-carbon control optimization method for distribution network based on carbon flow distribution according to claim 1 is characterized in that: The specific contents of establishing the first-stage low-carbon economic optimal power flow model with supply-side cost minimization as the objective function and power system constraints are as follows: establishing a first-stage objective function, establishing output constraints for distributed gas-fired units, establishing main grid power purchase constraints, establishing node voltage and line current constraints, establishing line power flow constraints, establishing node power balance constraints, establishing Ohm's law constraints, and establishing second-order cone relaxation constraints.

4. The three-stage low-carbon control optimization method for distribution network based on carbon flow distribution according to claim 1 is characterized in that: The specific contents of establishing the third-stage flexibility resource regulation model include: establishing a three-stage objective function, node load change constraints, load response balance constraints, new energy unit output constraints, and energy storage device constraints.

5. A three-stage low-carbon regulation and optimization system for distribution network based on carbon flow distribution, characterized in that A three-stage low-carbon regulation and optimization method for a distribution network based on carbon flow distribution as described in any one of claims 1 to 4 is implemented, comprising a module for establishing a low-carbon economic optimal power flow model of a distribution network, a carbon flow distribution module, a module for establishing a carbon cost model, and a module for establishing a flexibility resource regulation model connected in sequence; wherein, Establish a low-carbon economic optimal power flow model module for the distribution network: establish a first-stage low-carbon economic optimal power flow model with supply-side cost minimization as the objective function and under the constraints of the power system, obtain the distribution network benchmark power flow results, and pass them to the second stage for distribution network carbon profiling; Carbon flow distribution module: In the second stage, the node carbon potential is obtained by introducing the key matrix vectors of the node active flux matrix, branch power flow distribution matrix, and unit injection distribution matrix, and the real-time carbon emissions of each branch, node, and main grid of the distribution network are obtained to make a carbon portrait for the distribution network; Establish a carbon cost model module: In the second stage, a carbon cost model is established based on the carbon flow distribution of the distribution network, and finally the carbon emission cost obtained in the second stage is transmitted to the corresponding first and third stages; Establish a flexibility resource regulation model module: Establish a third-stage flexibility resource regulation model to optimize the allocation of flexibility resources.

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